To assess the proportion of women presenting with preterm delivery because of preeclampsia or placental insufficiency (PREPI) with anti-phospholipid antibodies (APLA).
This was a prospective cohort study conducted at an obstetrics and gynecology department. Women, aged 20-40 years, with preeclampsia who delivered before 34 weeks were cases while those who delivered before 34 weeks but did not have preeclampsia acted as controls. Both groups had APLA measured at diagnosis and 12-weeks postnatally. Anti-phospholipid antibody syndrome (APS) was diagnosed according to Sapporo’s criteria.
The study included 98 cases and 106 controls. Both cases and controls were similar in terms of age, gestational age and parity. The frequency of APS positivity was 17.3% in cases but only 3.8% in controls (p=0.001). Cases were more likely to be of Baloch ethnicity (34.7% vs. 11.3%, p=0.001), have a history of miscarriage (25.5% vs. 13.2%, p=0.026), use aspirin (p&lt;0.001) or low molecular weight heparin (p&lt;0.001), and be obese (p&lt;0.001) than controls. Cases were more likely to have lupus anticoagulant antibodies (82.4% vs. 75%).
Our study confirms a high prevalence of APLA in women who have preterm delivery due to PREPI. An opportunity to screen these women should be made, so that proper counselling can be given and future pregnancies can be managed in an appropriate and timely manner.
Our study confirms a high prevalence of APLA in women who have preterm delivery due to PREPI. An opportunity to screen these women should be made, so that proper counselling can be given and future pregnancies can be managed in an appropriate and timely manner.Introduction Currently, an estimated two thirds of the world population is water insufficient. As of 2015, one out of every five people in developing countries do not have access to clean sufficient drinking water. In an attempt to share the limited resource, water has been distributed at irregular intervals in cities in developing countries. Residents in these cities seek alternative water sources to supplement the inadequate water supplied. Some of these alternative sources of water are unsafe for human consumption, leading to an increased risk in water-borne diseases. Africa contributes to 53% of the diarrheal cases reported globally, with contaminated drinking water being the main source of transmission. Water-borne diseases like diarrhea, cholera, typhoid, amoebiasis, dysentery, gastroenteritis, cryptosporidium, cyclosporiasis, giardiasis, guinea worm and rotavirus are a major public health concern. The main objective of this scoping review is to map the available evidence to understand the sources of water among residents in cities in Africa and the relationship between clean water sufficiency and water-borne diseases in urban Africa. Methods and analysis The search strategy will identify studies published in scientific journals and reports that are directly relevant to African cities that have a population of more than half a million residents as of 2014 AND studies on the ten emerging water-borne diseases, which are diarrhea, cholera, typhoid, amoebiasis, dysentery, gastroenteritis, cryptosporidium, cyclosporiasis, giardiasis, guinea worm and rotavirus. https://www.selleckchem.com/products/cpypp.html Ethics and dissemination This scoping review did not require any formal ethical approval. The findings will be published in a peer-reviewed journal.The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1990-1992. The resource provides an informative and efficient setting for collecting data on the current coronavirus 2019 (COVID-19) pandemic. In early March 2020, a questionnaire was developed in collaboration with other longitudinal population studies to ensure cross-cohort comparability. It targeted retrospective and current COVID-19 infection information (exposure assessment, symptom tracking and reported clinical outcomes) and the impact of both disease and mitigating measures implemented to manage the COVID-19 crisis more broadly. Data were collected on symptoms of COVID-19 and seasonal flu, travel prior to the pandemic, mental health and social, behavioural and lifestyle factors. The online questionnaire was deployed across parent (G0) and offspring (G1) generations between 9 th April and 15 th May 2020. 6807 participants completed the questionnaire (2706 original mothers, 1014 original fathers/partners, 2973 offspring (mean age ~28 years) and 114 offspring partners). Eight (0.01%) participants (4 G0 and 4 G1) reported a positive test for COVID-19, 77 (1.13%; 28 G0 and 49 G1) reported that they had been told by a doctor they likely had COVID-19 and 865 (12.7%; 426 G0 and 439 G1) suspected that they have had COVID-19. Using algorithmically defined cases, we estimate that the predicted proportion of COVID-19 cases ranged from 1.03% - 4.19% depending on timing during the period of reporting (October 2019-March 2020). Data from this first questionnaire will be complemented with at least two more follow-up questionnaires, linkage to health records and results of biological testing as they become available. Data has been released as 1) a standard dataset containing all participant responses with key sociodemographic factors and 2) as a composite release coordinating data from the existing resource, thus enabling bespoke research across all areas supported by the study.Buccal bifurcation cyst (BBC) is a rare inflammatory odontogenic cyst, which commonly affects children in the first decade of life. We report a case of a seven-year-old healthy boy with bilateral BBC, which involved unerupted incomplete permanent mandibular first molars. A review of the literature in English language revealed few similar cases. We reviewed 16 manuscripts of bilateral mandibular BBC, reporting a total of 20 cases since 1970. The clinical features of bilateral mandibular BBC summarized here could assist specialists with an accurate diagnosis and provide patients with optimal management.Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data - a common problem in real-world data - without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers.